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The children

left behindA league table of inequality in child

well-being in the worlds rich countries

UNICEFInnocenti Research Centre

Report Card 9


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Innocenti Report Card 9 was written by Peter Adamson.

Part 1 of the Report draws on the analysis carried out by

Dominic Richardson, Candace Currie, Dorothy Currie, Chris Roberts

and Leonardo Menchini, and presented in theInnocenti Working Paper

2010-19 (available on the UNICEF Innocenti Research Centre (IRC)

website: www.unicef-irc.org).

Report Card 9 beneted greatly from research support by the Organisation

for Economic Cooperation and Development (OECD) and by the Health

Behaviour in School-aged Children (HBSC) International Coordinating

Centre. The OECD provided the statistical and distributional analysis

of child well-being indicators for material well-being and educational

outcomes. HBSC provided the statistical results for the analysis of

inequality in childrens health. Neither partner is responsible for the

interpretation of the results or for other ndings in this report. The project

was coordinated by the UNICEF IRC.

The UNICEF Innocenti Research Centre would like to acknowledgethe generous support forReport Card 9 provided by the Andorran,

Australian, Belgian, German, Swiss and United Kingdom National

Committees for UNICEF.

Any part of thisInnocenti Report Card may be freely reproduced using

the following reference:

UNICEF (2010), The Children Left Behind: A league table of inequality

in child well-being in the worlds rich countries,Innocenti Report Card 9,

UNICEF Innocenti Research Centre, Florence.

TheReport Card series is designed to monitor and compare the

performance of economically advanced countries in securing the rights

of their children.

The UNICEF Innocenti Research Centre in Florence, Italy, was

established in 1988 to strengthen the research capability of the

United Nations Childrens Fund (UNICEF) and to support its advocacy

for children worldwide.

The Centre helps to identify and research current and future areas of

UNICEFs work. Its prime objectives are to improve international

understanding of issues relating to childrens rights and to help facilitate

the full implementation of the United Nations Convention on the Rights of

the Child in all countries.

The Centres publications are contributions to a global debate on child

rights issues and include a wide range of opinions. The views expressed

are those of the author and researchers and do not necessarily reect the

policies or views of UNICEF.

United Nations Childrens Fund (UNICEF), November 2010

UNICEF Innocenti Research Centre

Piazza SS. Annunziata, 12

50122 Florence, Italy

Tel: (+39) 055 2033 0

Fax: (+39) 055 2033 220

[email protected]

www.unicef-irc.org


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UNICEF

Innocenti Research Centre

Whether in health, in education, or in material well-being, some

children will always fall behind the average. The critical question

is how far behind? Is there a point beyond which falling behind

is not inevitable but policy susceptible, not unavoidable butunacceptable, not inequality but inequity?

There are no widely agreed theoretical answers to these

questions. Report Card 9 seeks to stimulate debate on the issue

by introducing a common measure of bottom-end inequality.

This permits each countrys performance to be assessed according

to the standard of what the best-performing countries have been

able to achieve. Such a standard may not represent the best thatmay be aspired to in theory, but in practice it suggests a level

below which falling behind is manifestly not inevitable.

The Report Card series is premised on the belief that the true

measure of a nations standing is how well it attends to its children

their health and safety, their material security, their education

and socialization, and their sense of being loved, valued, and

included in the families and societies into which they are born.

Its common theme is that protecting children during their vital,vulnerable years of growth is both the mark of a civilized society

and the means of building a better future.

This ninth report in the series builds on previous issues by

focusing specically on those children in all OECD countries

who are at risk of being left behind of being neither included

nor protected by the wealthy societies in which they live.

I N N O C E N T I R E P O R T C A R D 9 1


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Material well-being rank Education well-being rank Health well-being rank

Switzerland 1 Finland 1 Netherlands 1

Iceland 2 Ireland 2 Norway 2

Netherlands 3 Canada 3 Portugal 3

Denmark 4 Denmark 4 Germany 4

France 5 Poland 5 Switzerland 5

Finland 6 Hungary 6 Belgium 6Austria 7 Sweden 7 Ireland 7

Norway 8 Netherlands 8 Denmark 8

Sweden 9 Spain 9 Canada 9

Germany 10 Iceland 10 Czech Republic 10

Czech Republic 11 Norway 11 United Kingdom 11

Luxembourg 12 Switzerland 12 Slovakia 12

Ireland 13 United Kingdom 13 Austria 13

Spain 14 Portugal 14 Sweden 14

Belgium 15 Slovakia 15 France 15

Portugal 16 Luxembourg 16 Finland 16

Canada 17 Czech Republic 17 Iceland 17

Greece 18 Greece 18 Poland 18

United Kingdom 19 United States 19 Luxembourg 19

Italy 20 Germany 20 Greece 20

Poland 21 Italy 21 Spain 21

Hungary 22 Austria 22 United States 22

United States 23 France 23 Italy 23

Slovakia 24 Belgium 24 Hungary 24

inequality lower thanOECD average

inequality close toOECD average

inequality higher thanOECD average

Higher score = greater equality

8Denmark

Finland

Netherlands

Switzerland

7

Iceland

IrelandNorway

Sweden

6

Austria

Canada

France

Germany

Poland

Portugal

5

Belgium

Czech Republic

Hungary

Luxembourg

Slovakia

Spain

United Kingdom

3Greece

Italy

United States

Fig. 1a A league table of inequality in child well-being

The table summarizes the ndings of Report Card 9, ranking 24 OECD countries by their

performance in each of three dimensions of inequality in child well-being.

Fig. 1b The overall record

Figure 1b ranks each country by its overall

inequality record. Three points have been

awarded for a better than average

performance, 2 points for a performance

at or close to the OECD average, and1 point for a below average performance

(see note for denitions). Countries in

alphabetical order within groups.

Figs. 1a and 1b are limited to the 24 OECD countries with available data for all three

dimensions of inequality in child well-being.

Note: To compare the inequality performance of the featured countries in each

dimension of child well-being, inequality scores for the individual indicators usedare rst converted to standard scores (i.e. inequality is measured in standard

deviations from the OECD unweighted average). The standardized scores are then

averaged to arrive at an inequality score for each dimension. For purposes of Figs.

1a and 1b, inequality close to average is dened as a score within the range of

-0.5 to +0.5 standard deviations from the OECD average. inequality lower than

OECD average is dened as having a standard deviation score greater than +0.5

from the OECD unweighted average. inequality higher than OECD average is

dened as having a standard score of less than -0.5 from the OECD unweighted

average.

Source:See page 30 (Data forReport Card 9: the surveys) for data sources used in

the measurement of inequality in the different dimensions of childrens well-being.

A league table of inequality in child well-beingin the worlds rich countries

T E C D R E N E T E N D

2 I N N O C E N T I R E P O R T C A R D 9


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quality-of-life requires appropriate measures

of inequality with each of these measures

being signicant in itself and none claiming

absolute priority over the others, says the

Commission on the Measurement of

Economic Performance and Social

Progress established in 2008 by the

President of France.*

Figure 1a therefore compares 24

OECD countries according to their

performance in limiting bottom-endinequality in three dimensions of

childrens well-being. Its rankings

conrm the Commissions view that

no one indicator can stand as an

adequate proxy for the others.

Measuring the gap

Depending on the available data, two

different methods are proposed for

estimating how far behind children

are being allowed to fall.

The rst compares the position of the

child at the 10th percentile (i.e. the

child at a lower point than 90% of

children in the society) with the child

at the 50th percentile (the median

position). The degree of inequality is

measured by the gap between the two,

expressed as a percentage of the

median position.

The second method (employed when

survey data are not suitable for analysis

by percentiles) compares the level of

well-being of the child at the median

with the average level of all those who

fall below the median.

Different geographical and historical

circumstances may help to explain

different degrees of inequality. And it

is of course a truism that there will

always be a bottom 10% in any

country and that 50% of children will

always fall below the national median.

In this sense, a degree of falling behind

is obviously inevitable. The critical

question is how far behind? Is there a

point beyond which falling behind is

not inevitable but policy susceptible,

not unavoidable but unacceptable, not

inequality but inequity?

There are no widely agreed theoreticalanswers to these questions. But

international comparison can help

to establish practical answers by

measuring falling behind according

to the standard of what the best-

performing OECD countries have

already achieved. This benchmark may

not represent the very best that can be

aspired to, but it does establish a level

below which bottom-end inequality is

manifestly not inevitable.

If, for example, the gap in educational

achievement between students at the

10th and 50th percentiles is

signicantly wider in France or

Belgium than in Finland or Ireland

(Figure 3d) then it seems clear that

the children at the 10th percentile in

French and Belgian schools are falling

further behind the median than is

necessary. The difference between the

best performing countries and the rest

of the OECD nations can therefore

be read as a minimum measure of the

extent to which falling behind is

policy-susceptible the extent to

which it is not unavoidable but unjust.

International comparison therefore sets

each nations performance not against

an abstract concept of equality but

against the practical benchmark of

what other nations at similar levels

of economic development have already

achieved. It therefore provides a realistic

measure of the scope for improvement.

* The Commission is chaired by Joseph Stiglitz, Amartya Sen and Jean-Paul Fitoussi.



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Mexico

Chile (2006)

Republic of Korea (2007)

Australia

Other OECD countries

OECD average

Greece

Portugal

Spain

Canada (2005)

Italy

Poland

Belgium

United Kingdom

Slovakia

Germany

Ireland

Luxembourg

Hungary

Switzerland (2007)

Czech Republic

Finland

France (2007)

Netherlands

Sweden

Iceland

Austria

Denmark

Norway

61.5

60.8

59.4

51.0

46.9

56.6

56.2

56.0

55.7

54.1

51.2

50.6

50.1

48.9

48.1

47.4

46.4

44.6

44.3

43.7

41.9

41.6

41.5

41.2

40.2

40.0

39.7

39.4

0 10 20 30 40 50 60 70

Gap between the child at the 10th percentile and the child at the 50th percentile (as % of 50th percentile)

The rst of the three dimensions of

inequality in childrens well-being

considered here is inequality in

childrens material well-being.

Child poverty is about more than

poverty of income. It is also about

poverty of opportunity and expectation,

of cultural and educational resources,

of housing and neighbourhoods, of

parental care and time, of local services

and community resources. But fromthe childs point of view, these different

dimensions of poverty are rarely

separate. Family circumstance,

employment and income, health and

education systems, and the local

environment all play interacting roles

in determining well-being.

No internationally comparable data

are currently available to capture this

complexity. But rather than relying

on income data alone, inequality in

childrens material well-being is

measured here by three indicators

for which suitable data are available

household incomes, access to basic educational

resources, and housing living space.

Household income

Calculations of income inequality

for children are based on the disposable

incomes of households with children

aged 0 to 17 (after adding benets,

deducting taxes, and making an

adjustment for the economies of scale

available to larger families). To measure

inequality at the bottom-end of the

distribution, the income of the child

at the 50th percentile (the median)

is compared with the income of the

child at the 10th percentile (i.e. poorer

than 90% of children). How far behind

are the poorest children being allowed to

fall? is then measured by the gap

between the two.

As Figure 2a shows, household income

inequality for children is lowest in

Norway, with the Nordic countries and

the Netherlands taking six of the top

eight places in the table. At the other

extreme, Italy, Canada Spain, Portugal

and Greece are seen to have the highest

levels of child income inequality. Data

on household disposable income are not

available for the United States.*

Basic educational resources

The second measure used to compare

inequality in material well-being is

access to basic educational resources.

Again, the same question is asked

how far behind are the least advantaged

children being allowed to fall?

Fig. 2a nequality in material well-being: incomeThe chart shows inequality at the bottom-end of the distribution in disposable income

available to children in 27 OECD countries. Calculations are based on the incomes of

households with children aged 0 to 17 (after adding benets, deducting taxes, and making

an adjustment for the economies of scale available to larger families). For each country,

the measure of bottom-end inequality used is the gap between the income of the child at

the 50th percentile (the median level) and the income of the child at the 10th percentile

(i.e. the child who is poorer than 90% of children).

The bar chart shows how far the children at the 10th percentile are falling behind

(expressed as a percentage of median income in households with children).

Notes: Other OECD countries are listed separately because data limitations prevent their inclusion in the

overview tables for each dimension of child well-being. The OECD average is an unweighted average for the23 countries included in the main league table.

Sources: EU SILC 2008. Data for France are from EU SILC 2007. See page 30 (Data forReport Card 9: the surveys)

for more detailed notes on country data including sources for Australia, Canada, Chile, Mexico, the Republic of

Korea, and Switzerland.

M A T E R A N E A T

* Using gross (pre-tax) household income, the income available to the child at the 10th percentile in the

United States is approximately 70% below the income available to a child at the median.



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Mexico

New Zealand

Turkey

Chile

Japan

Australia

Republic of Korea

OECD average

Slovakia

Greece

United Kingdom

Hungary

BelgiumUnited States

Germany

Poland

Norway

Ireland

Portugal

Spain

France

Iceland

Czech Republic

Italy

Sweden

Canada

Finland

AustriaLuxembourg

Netherlands

Switzerland

Denmark

22.5

22.1

22.0

21.9

19.9

18.9

14.2

15.2

25.9

22.6

21.0

20.7

19.919.2

18.5

18.1

16.3

16.2

16.1

14.9

14.3

14.0

13.9

13.3

12.0

11.3

11.3

10.99.5

8.8

8.1

7.9


0 105 15 20 25 30

Figure 2b attempts an answer by

drawing on survey data from the

Programme of International Student

Assessment (PISA).

In the 2006 PISA survey (see page 30),

a representative sample of 15-year-old

students in OECD countries was askedwhich of the following were available

in their own homes:

a desk

a quiet place to study

a computer for school work

educational software

an internet connection

a calculator

a dictionary

school textbooks.

The resulting scores registered on

a scale of 0 to 8 do not lend

themselves to analysis by percentile.

Inequality is therefore measured by the

gap between the score of the child at

the median and the average score of all

children who fall below the median.

The results are presented in Figure 2b.

The availability of computers and

internet access depends to some extent

on the level of economic development

in each country; even poor children

in very wealthy countries, for example,

may have access to most or all of the

items on the home educational

resources list. The median score

therefore differs from country to

country. But the focus here is on

inequality on the gap between the

median score (column 2) and the

average score below the median

(column 3). Column 4 shows the

difference between the two and the

chart represents the inequality gap as

a percentage of the median.

Northern European countries again

dominate the top of the table. The

lowest placed Nordic country, Norway,

posts an equality score close to the

average for the OECD as a whole.

At the foot of the table, the United

Kingdom, Greece, and Slovakia show

the highest levels of inequality in

access to basic educational resources.

Fig. 2b nequality in material well-being: educational resources

15-year-olds students in each country were asked which of the following were available at home: a desk, a quiet place to study,

a computer for school work, educational software, an internet connection, a calculator, a dictionary, school textbooks.

Inequality was measured by comparing each countrys median score (column 2) with the average score of those below

the median (column 3). Column 4 shows the difference between the two as an absolute number of missing educational items.

The bar chart on the right shows the inequality gap (as a percentage of the median for each country).

Notes: Other OECD countries are listed separately because data limitationsprevent their inclusion in the overview tables for each dimension of child well-

being. The OECD average is an unweighted average for the 24 countries included

in the main league table.

Source: PISA 2006 (see page 30).

Educational items

(range 0-8)Median

Average

below the

median

Average

absolute

gap

Gap between the child at the 10th percentile and the

child at the 50th percentile (as % of 50th percentile)

Denmark 7 6.4 0.6

Switzerland 7 6.4 0.6

Netherlands 7 6.4 0.6

Luxembourg 7 6.3 0.7Austria 7 6.2 0.8

Finland 7 6.2 0.8

Canada 7 6.2 0.8

Sweden 7 6.2 0.8

Italy 7 6.1 0.9

Czech Republic 7 6.0 1.0

Iceland 8 6.9 1.1

France 7 6.0 1.0

Spain 7 6.0 1.0

Portugal 7 5.9 1.1

Ireland 7 5.9 1.1

Norway 8 6.7 1.3

Poland 7 5.7 1.3

Germany 8 6.5 1.5

United States 7 5.7 1.3Belgium 8 6.4 1.6

Hungary 7 5.6 1.4

United Kingdom 8 6.3 1.7

Greece 6 4.6 1.4

Slovakia 7 5.2 1.8

OECD average 7.2 6.1 1.1


Republic of Korea 7 6.0 1.0

Australia 8 6.5 1.5

Japan 6 4.8 1.2

Chile 5 3.9 1.1

Turkey 5 3.9 1.1

New Zealand 8 6.2 1.8

Mexico 5 3.9 1.1



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Mexico

Chile (2006)

Australia


OECD average

Hungary

Italy

United States (2007)

Poland

Canada (2006)Luxembourg

Slovakia

United Kingdom

Sweden

Denmark

Austria

Czech Republic

Portugal

Finland

Belgium

Norway

Ireland

Netherlands

France (2007)

SpainGreece

Switzerland (2007)

Germany

Iceland

45.3

26.5

11.8

20.8

33.4

31.8

29.3

28.6

27.526.2

24.9

24.4

24.4

23.0

22.9

22.2

19.6

19.3

19.0

18.8

17.7

17.6

14.5

14.514.0

9.1

8.9

8.8

0 10 20 30 40 50

Living space

The third measure of material well-

being is living space dened as the

number of rooms per person in

households with children aged 0 to 17

(not counting corridors, kitchens, and

bathrooms). Although only an

approximate measure of housingconditions, space in the home is a

constant and important factor in

young peoples lives. In the OECD

countries as a whole, one child in

three is estimated to be living in

overcrowded conditions.iii

Figure 2c draws again on survey data

to estimate inequality in living space.

As with educational resources, the

measure used is the gap between the

living space score at the median and

the average score of children below

the median. By this measure,

Denmark, Switzerland and the

Netherlands can be seen to have the

lowest levels of inequality in childrens

living space (along with Australia,

which is among the countriesexcluded from the main tables

because data are not available for all

three dimensions of child well-being).

At the bottom of the table, inequality

is highest in the United States, Italy

and Hungary.

Material inequality:

an overview

Figures 2d and 2e combine the three

measures used household income,

access to educational resources, and

living space. For each country, and

for each indicator, the inequality

scores have been set on a common

scale in which 100 represents the

OECD average and 10 represents

one standard deviation (a commonly

used measure of how spread out theitems being measured are in relation

to the average for the group as a

whole). The individual indicator

scores are then averaged to provide

the overview of inequality in

childrens material well-being

presented in Figure 2d.

Switzerland has the least inequality,

closely followed by Iceland and the

Netherlands.

Fig. 2c nequality in material well-being: housing living space

Housing living space is dened as the number of rooms per person in households with children (not counting corridors, kitchens, and

bathrooms). Inequality is measured by the gap between the score at the median (column 2) and the average score of all children below the

median (column 3). Column 4 shows the difference between the two. The bar chart on the right shows the inequality gap (as a percentage

of the median).

Notes: Other OECD countries are listed separately because data limitations

prevent their inclusion in the overview tables for each dimension of child well-


in the main league table.

Sources: EU SILC 2008. Data for France are from EU-SILC 2007. See page 30 (DataforReport Card 9: the surveys) for more detailed notes on individual country data

including sources for Australia, Canada, Chile, Mexico, the Republic of Korea,

Switzerland and the United States.

Living space

Rooms per personMedian

Average

below the

median

Average

absolute

gap

Gap between the average below the median

and the median (as % of median)

Iceland 1.00 0.91 0.09

Germany 1.00 0.91 0.09

Switzerland (2007) 1.00 0.91 0.09

Greece 0.80 0.69 0.11Spain 1.25 1.08 0.18

France (2007) 1.00 0.85 0.15

Netherlands 1.25 1.03 0.23

Ireland 1.25 1.03 0.23

Norway 1.20 0.97 0.23

Belgium 1.20 0.97 0.23

Finland 1.20 0.97 0.23

Portugal 1.00 0.80 0.20

Czech Republic 0.80 0.62 0.18

Austria 1.00 0.77 0.23

Denmark 1.20 0.92 0.28

Sweden 1.20 0.91 0.29

United Kingdom 1.20 0.91 0.29

Slovakia 0.75 0.56 0.19

Luxembourg 1.25 0.93 0.33Canada (2006) 1.50 1.10 0.41

Poland 0.67 0.47 0.19

United States (2007) 1.25 0.89 0.36

Italy 1.00 0.68 0.32

Hungary 0.75 0.50 0.25



Australia 1.00 0.88 0.12

Chile (2006) 0.75 0.55 0.20

Mexico 0.50 0.28 0.23



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Slovakia

United States

Hungary

Poland

Italy

United Kingdom

Greece

Canada

Portugal

Belgium

Spain

Ireland

Luxembourg

Czech Republic

Germany

Sweden

Norway

Austria

Finland

France

Denmark

Netherlands

Iceland

Switzerland

85 90 95 100 105 110 115

inequality higher than

OECD-24 average

inequality close to

OECD-24 average

inequality lower than

OECD-24 average

Slovakia

United States**

Hungary

Poland

Italy

United Kingdom

Greece

Canada

Portugal

Belgium

Spain

Ireland

Luxembourg

Czech Republic

Germany

Sweden

Norway

Austria

Finland

France

Denmark

Netherlands

Iceland

Switzerland

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0


OECD-24 average


OECD-24 average

income

educational items

living space

A second summary table (Figure 2e)

shows the individual contributions of

the three indicators, allowing countries

to see their strengths and weaknesses.

Countries such as Germany, Belgium,

the United Kingdom, Greece and

Slovakia, for example, are let down by

higher than average inequality inaccess to basic educational resources.

Spain, Canada, Portugal and Greece

lose ranking places by virtue of higher

than average levels of household

income inequality.

These three measures of bottom-end

inequality in childrens material well-

being are neither ideal norcomprehensive. But they are the best

available for the purposes of

international comparison. Rather than

recording material well-being solely

by the percentage of children in

households below a given income

threshold, they attempt a more

rounded measure of how far behind

the least advantaged children are beingallowed to fall.

Fig. 2d nequality in material well-being: an overview

Figure 2d combines the three measures of inequality in childrens

material well-being (income, educational items, living space) into

an overview for the 24 OECD countries with available data. For

each country, the inequality scores of the three indicators of

material inequality have been standardized, combined and placed

on a common scale in which 100 represents the OECD unweighted

average and 10 is equal to one standard deviation.*

Fig. 2e nequality in material well-being: a breakdown

Figure 2e presents the same information as Fig 2d but shows the

individual contributions of the three inequality indicators used. For

each indicator, the length of the bar represents each countrys

distance above or below the OECD 24 average (again measured in

standard deviations above or below that average). This allows

countries to see individual strengths and weaknesses.

* A standard deviation is a measure of the spread of the distribution aroundits average.

Sources: See individual Figs. 2a, 2b, 2c. See also Figure 2e for the standardized

inequality measure used for the three individual indicators of inequality in

childrens material well-being.

** No data are available on household disposable income for the United States.Sources: See individual Figs. 2a, 2b, and 2c.



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Mexico

Chile

Japan

New Zealand

Turkey

Australia

Republic of Korea


OECD average

Belgium

Italy

Greece

Czech Republic

Germany

SlovakiaFrance

Austria

Norway

Luxembourg

Portugal

United Kingdom

Iceland

Poland

Hungary

Spain

Netherlands

Sweden

Switzerland

Canada

Ireland

Denmark

Finland

31.3

30.0

28.6

27.9

26.8

25.1

21.8

28.1

32.5

32.1

31.5

31.4

31.1

31.030.7

30.3

29.7

29.3

29.2

28.4

27.6

27.0

26.8

26.6

26.5

26.2

26.1

24.8

24.2

24.2

19.9

0 5 10 15 20 25 30 35

E D C A T O N A N E A T

The second dimension of inequality

considered here is inequality in young

peoples educational achievements.

The data are drawn from the

Programme of International Student

Assessment (PISA) which regularly

tests a nationally representative sampleof 15-year-old students* in more than

40 countries. The aim is to test and

compare prociency in reading, maths

and science.

As with income, the inequality measure

used is the gap between test scores at

the 10th and 50th percentiles. Figures

3a, 3b and 3c present the results.

Figure 3d combines the three measures

into an overview. Again, each countrys

score on each indicator has been set

on a common scale in which 100

represents the unweighted OECD

average and 10 represents one standard

deviation above or below that average.

This allows each countrys

performance to be measured in

relation to both the average and thedegree of variability for the OECD

as a whole.

Fig. 3a nequality in reading literacy

Using PISA scores for reading literacy of 15-year-old students, Figure 3a measures educational inequality in each country by comparing

the score of the student at the 50th percentile (the median) with the score of the student at the 10th percentile (i.e. lower than 90% of all

scores). The bar chart shows the gap between the two (expressed as a percentage of the median).

Notes: Other OECD countries are listed separately because data limitationsprevent their inclusion in the overview tables for each dimension of child well-


in the main league table. Reading literacy data for the USA are missing.


Reading literacy

50th

percentile

(median) score

10th

percentile

score

Absolute gap

(50th percentile

10th percentile)

Gap between the child at the 10th percentile and the child

at the 50th percentile (as % of 50th percentile)

Finland 550 441 109

Denmark 499 378 121

Ireland 522 395 127

Canada 534 402 132

Switzerland 506 373 133

Sweden 513 378 135

Netherlands 515 379 136

Spain 468 343 125

Hungary 490 359 131

Poland 513 374 139

Iceland 491 356 135

United Kingdom 501 359 142

Portugal 479 339 140

Luxembourg 487 344 143

Norway 492 346 146

Austria 499 348 151

France 499 346 153Slovakia 473 326 147

Germany 508 350 158

Czech Republic 489 335 154

Greece 469 321 148

Italy 478 325 153

Belgium 515 347 168

OECD average 500 359 141


Republic of Korea 563 440 123

Australia 519 388 131

Turkey 450 330 120

New Zealand 528 381 146

Japan 505 361 144

Chile 443 310 133

Mexico 415 285 130

*The survey samples only 15-year-olds who are attending school. It may therefore not fully represent marginalized groups in some OECD countries.



12/40

Mexico

Chile

Turkey

New Zealand

Japan

Republic of Korea

Australia


OECD average

BelgiumAustria

Czech Republic

France

Greece

Germany

Portugal

Luxembourg

Slovakia

Switzerland

United States

Spain

Norway

Hungary

Sweden

United Kingdom

Iceland

Netherlands

Poland

Canada

DenmarkIreland

Finland

Italy

26.4

25.9

23.8

23.3

23.2

22.5

22.0

24.1

27.927.0

26.2

26.2

26.1

25.8

25.5

25.2

25.0

24.8

24.2

24.1

23.9

23.1

23.1

22.9

22.8

22.8

22.4

21.5

21.421.2

19.3

26.1

0 5 10 15 20 25 30

New Zealand

Japan

Australia

Chile

Mexico

Republic of Korea

Turkey


OECD average

United States

France

Belgium

United Kingdom

Luxembourg

Germany

Austria

Switzerland

Italy

Iceland

Greece

Netherlands

Norway

Czech Republic

Denmark

Portugal

Slovakia

Spain

Sweden

Ireland

Canada

Poland

Hungary

Finland

27.1

26.4

25.6

25.6

24.9

23.4

21.9

25.5

28.4

28.3

27.9

27.4

27.0

26.9

26.8

26.7

26.3

26.2

26.0

25.5

25.2

25.2

25.0

24.9

24.8

24.7

24.6

24.4

24.1

23.4

23.2

20.0

0 5 10 15 20 25 30

Fig. 3b nequality in maths literacy

Using PISA scores for maths literacy of 15-year-old students, Figure 3b measures educational inequality in each country by comparing

the score of the student at the 50th percentile (the median) with the score of the student at the 10th percentile (i.e. lower than 90% of all

scores). The bar chart shows the gap between the two (expressed as percentage of median).

Fig. 3c nequality in science literacy

Using PISA scores for science literacy of 15-year-old students, Figure 3c measures educational inequality in each country by comparing

the score of the student at the 50th percentile (the median) with the score of the student at the 10th percentile (i.e. lower than 90% of allscores). The bar chart shows the gap between the two as a percentage of the median.

Notes: Other OECD countries are listed separately because data limitationsprevent their inclusion in the overview tables for each dimension of child well-being.

The OECD average is an unweighted average for the 24 countries included in the

main league table. Source: PISA 2006 (see page 30).



main league table. Source: PISA 2006 (see page 30).

Maths literacy

50th

percentile

(median) score

10th

percentile

score

Absolute gap

(50th percentile

10th percentile)



Finland 550 444 106

Ireland 503 396 107Denmark 514 404 110

Canada 529 416 113

Poland 495 384 111


Iceland 507 391 116


Sweden 503 387 116

Hungary 490 377 113

Norway 490 373 117

Spain 482 366 116

United States 472 358 114


Slovakia 494 370 124



Germany 505 375 130

Greece 461 341 120

Italy 462 341 121

France 499 369 130


Austria 511 373 138Belgium 528 381 147





Japan 526 404 122


Turkey 415 316 99

Chile 408 302 106

Mexico 406 299 107

Science literacy

50th

percentile

(median) score

10th

percentile

score

Absolute gap

(50th percentile

10th percentile)



Finland 566 453 113

Hungary 506 388 117

Poland 498 381 117

Canada 540 410 130

Ireland 510 385 124

Sweden 505 381 124

Spain 491 370 121

Slovakia 489 368 121


Denmark 498 373 125


Norway 488 365 123


Greece 477 353 124

Iceland 493 364 129

Italy 477 351 126


Austria 516 378 138

Germany 521 381 140



Belgium 518 374 145

France 501 359 142

United States 488 349 139



Turkey 416 325 91


Mexico 407 306 102

Chile 434 323 111


Japan 539 396 142


1 0 I N N O C E N T I R E P O R T C A R D 9


13/40

Belgium

France

Austria

Italy

Germany

United States

Greece

Czech Republic

Luxembourg

Slovakia

Portugal

United Kingdom

Switzerland

Norway

Iceland

Spain

Netherlands

Sweden

Hungary

Poland

Denmark

Canada

Ireland

Finland

80 85 90 95 100 105 110 115 120 125 130

inequality higher

than average

inequality close

to average

inequality lower

than average

Belgium

France

Austria

Italy

Germany

United States**

Greece

Czech Republic

Luxembourg

Slovakia

Portugal

United Kingdom

Switzerland

Norway

Iceland

Spain

Netherlands

Sweden

Hungary

Poland

Denmark

Canada

Ireland

Finland

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0


OECD-24 average


OECD-24 average

reading

maths

sciences

Figure 3e breaks down this overview

in order to show how bottom-end

inequality in the three different kinds

of literacy contributes to each

countrys overall inequality score.

No trade off

Such measurements serve to informtwo commonly contested issues.

First, they undermine the argument

that steady progress towards equality

of opportunity in education means

that differences in educational

outcomes are now mostly a reection

of the distribution of natural abilities.

As Figures. 3a, 3b and 3c show,

different OECD countries have very

different patterns of bottom-end

inequality in educational outcomes;and it is reasonable to assume that this

is the result not of differences in the

distribution of natural abilities but of

differences in policies which, over

time, limit the extent to which less

able students fall behind. Figure 3d, for

example, shows that lower-achieving

students in Finland, Ireland and

Canada are far less likely to fall a

long way behind their peers than are

students in Austria, France or Belgium.

The pattern of bottom-end inequality

in educational outcomes therefore

reects more than the lottery of birth

and circumstance. It may reect

Fig. 3d Educational inequality: an overview


educational outcomes (in reading, maths and science literacy) into

an overview for 24 OECD countries. For each country, the scores

on the three indicators have been standardized, averaged, and

placed on a common scale in which 100 represents the OECD

unweighted average and 10 is equal to one standard deviation.*

Fig. 3e Educational inequality: a breakdown

Figure 3e presents the same information as Figure 3d but shows

the individual contributions of the three inequality indicators used.

For each indicator, the length of the bar represents each countrys




* A standard deviation is a measure of the spread of the distribution around its

average.

Sources: See Figs. 3a, 3b, and 3c. See also Fig 3e for the standardized inequality

measure used for the three individual indicators of inequality in educational

well-being.

** Reading literacy data for the United States are missing.

Sources: See individual Figs. 3a, 3b, and 3c.

I N N O C E N T I R E P O R T C A R D 9 1 1


14/40

15 20 25 30 35

400

420

440

460

480

500

520

540

560

580

Medianscore

% gap between 50th and 10th percentiles

15 20 25 30 35

500

520

540

560

580

600

620

640

660

680

90th

percentiles

core

% gap between 50th and 10th percentiles

Notes: Blue vertical and horizontal lines indicate unweighted OECD average (30 countries). Trend line obtained by linear regression. Data for the United States are missing.

For country abbreviations see page 33.


Notes: Blue vertical and horizontal lines indicate unweighted OECD average (30 countries). Trend line obtained by linear regression. Data for the United States are missing.For country abbreviations see page 33.


differences in national efforts to

reduce socio-economic disadvantage.

Or it may reect efforts to weaken

the link between socio-economic

disadvantage and school

achievement (children whose

mothers did not complete secondary

school, for example, are atsubstantially greater r isk of having

low reading literacy scores, but that

risk is two or three times greater in

some countries than in others.)iv

It is likely, also, that different degrees

of inequality reect different

degrees of policy concern, over time,

for those at risk of falling behind.

Second, international comparisons of

inequality in educational outcomes also

inform the issue of whether a trade-off

must be made between investing in

low-achieving students and maximizing

the potential of those in the higher

reaches of the ability range. Figure 3f(i)

suggests an answer to this question byshowing that there is no relationship

between greater inequality and better

performance at the median. In fact the

most unequal countries tend towards

slightly lower scores at the 50th

percentile. The two countries with the

lowest bottom-end inequality in reading

literacy, Finland and South Korea, are

also the two countries with the highest

median levels of educational achievement.

A child born in either of these countries

therefore has both a lower chance of

falling a long way behind his or her peers

and a higher chance of scoring above the

average reading literacy mark for the

OECD as a whole.

Figure 3f(ii) shows that the point holds

when we look at performance of the

highest-achieving students. Again, the

countries with better results at the 90th

percentile of achievement tend to be

the countries with the lowest levels of

bottom-end inequality.

Fig. 3f(i) ottom-end inequality and median achievement

The chart compares inequality in reading literacy with median level scores for reading literacy in 30 OECD countries.

Fig. 3f(ii) ottom-end inequality and top-end achievement

The chart compares inequality in reading literacy with scores at the 90th percentile of achievement in 30 OECD countries.



15/40

Turkey


OECD average

United States

Greece

Luxembourg

Hungary

Italy

Poland

Iceland

Sweden

Spain

Finland

Canada

Slovakia

FranceCzech Republic

Belgium

Switzerland

United Kingdom

Norway

Ireland

Denmark

Germany

Portugal

Austria

Netherlands

27.8

23.6

28.0

27.0

26.9

26.8

26.3

25.8

25.8

25.5

24.3

24.3

24.3

23.6

23.323.1

23.1

22.9

22.4

21.5

20.8

20.7

20.5

20.4

20.4

19.2

0 5 10 15 20 25 30

E A T N E A T

The third and last dimension of child

well-being in which the data permit

cross national measurement of

inequality is health.

Again, three indicators are used:

childrens self-reported health

complaints; healthy eating; and

frequency of vigorous physical activity.

All three are well-established markers

for childrens current and future health.

The data are derived from the 2005-

2006 round ofHealth Behaviour in

School-aged Children, a World Health

Organization collaborative study

which regularly surveys the health

behaviours of schoolchildren at ages

11, 13 and 15 in 41 countries of

Europe and North America.

Self-reported health

Among many other questions,

participants in the HBSC survey were

asked how often in the previous six

months they had experienced the

following problems:

headache

stomach ache

feeling low

feeling irritable

feeling bad tempered

feeling nervous

having difculty getting to sleep

feeling dizzy.

The answers were transferred onto

a scale ranging from 0 (frequent

occurrences of all seven complaints)

to 28 (no health complaints).

Figure 4a uses these scores to estimate

the degree of inequality in childrens

self-reported health. As before, the

measure used is the gap between each

countrys median score (column 2) and

the average score of all children below

Fig. 4a ealth inequality: self-reported health complaints

The 2005-2006 HBSC survey (see page 30) asked 11, 13 and 15-year-old students how often in the previous six months they had

experienced the following problems: headache, stomach ache, feeling low, feeling irritable, feeling bad tempered, feeling nervous,

having difculty getting to sleep, feeling dizzy. The answers were transferred onto a scale ranging from 0 (frequent occurrences of

all seven complaints) to 28 (no health complaints).

Inequality was then measured by comparing each countrys median score (column 2) with the average score of those below the

median (column 3). Column 4 shows the difference between the two. The bar chart on the right shows the inequality gap (as a

percentage of the median).



main league table. Source: HBSC 2005-2006 (see page 30).

Health complaints

(range 0-28)Median

Average

below the

median

Average

absolute

gap




Austria 25.0 19.9 5.1

Portugal 25.0 19.9 5.1

Germany 23.0 18.3 4.7

Denmark 24.0 19.0 5.0

Ireland 23.0 18.2 4.8

Norway 23.0 18.1 4.9


Switzerland 22.0 17.0 5.0

Belgium 23.0 17.7 5.3

Czech Republic 21.0 16.2 4.8France 21.0 16.1 4.9

Slovakia 20.0 15.3 4.7

Canada 22.0 16.7 5.3

Finland 22.0 16.7 5.3

Spain 23.0 17.4 5.6

Sweden 22.0 16.4 5.6

Iceland 21.0 15.6 5.4

Poland 22.0 16.3 5.7

Italy 19.0 14.0 5.0

Hungary 21.0 15.4 5.6

Luxembourg 22.0 16.1 5.9

Greece 22.0 16.1 5.9

United States 22.0 15.8 6.2



Turkey 18.0 13.0 5.0



16/40

Turkey


OECD average

Hungary

Finland

Iceland

Ireland

United States

Italy

Spain

Austria

Denmark

Germany

LuxembourgSlovakia

Greece

Switzerland

United Kingdom

Portugal

Norway

Czech Republic

Sweden

France

Poland

Canada

Belgium

Netherlands

38.1

42.6

50.5

49.2

47.1

46.7

46.5

45.9

45.8

45.1

44.5

44.5

44.243.5

42.5

41.9

41.8

40.8

40.3

39.7

39.0

37.8

37.1

36.6

35.4

35.4

0 10 20 30 40 50 60

the median (column 3). The inequality

gap is presented both as an absolute

difference between the two scores

(column 4) and as a bar chart showing

the bottom-end inequality gap as a

percentage of each countrys median.

Self-reporting has limitations as anindicator of health status. Cultural

differences, for example, may play a part

in explaining differences between each

countrys average score (although self-

reporting by children has been shown

to be a good predictor of adult health

outcomesv). But the focus here is not

on averages but on the inequalities

revealed by comparing each countrys

median score with the average score

below the median.

Again it is noticeable that the countries

with the highest median levels of health

the Netherlands, Austria, and Portugal

are also the countries with the

lowest levels of health inequality.

Healthy eating and

vigorous physical activity

The second and third indicators

available for the measurement of

bottom-end inequalities in childrenshealth are based on HBSC survey

data under the headings of healthy

eating and vigorous physical activity.

Healthy eating is basic to a childs

normal growth and development and

to long-term health. Unhealthy

eating, by contrast, is associated with

a wide range of immediate and

long-term health problems including

obesity, type 2 diabetes, and cardio-

vascular disease.vi A key componentof healthy eating is the inclusion of

fruit and vegetables in a childs

daily diet.

Regular exercise in adolescence

also brings short and long-term health

benets and is positively associated

with cognitive development,

emotional well-being, and even

academic achievement.vii For children

and adolescents, the World Health

Organization recommends60 minutes of moderate to vigorous

physical exercise every dayviii

(a recommendation that is not widely

followed; taking an unweighted

average of the 41 countries included

in the HBSC study, only 12%

of 15-year-old girls and 20% of

15-year-old boys report taking an

hour of moderate to vigorous

physical activity every dayix).

In both cases, HBSC survey datahave been translated into scores for

healthy eating (on a scale of 0 to 14)

and frequency of vigorous physical

Fig. 4b ealth inequality: healthy eating

The 2005-2006 HBSC survey asked 11, 13 and 15-year-old students how often they ate fruit and vegetables. The answers were converted

into a healthy eating score on a scale of 0 (no fruit or vegetable consumption) to 14 (daily consumption of both fruit and vegetables).




Notes: Other OECD countries are listed separately because data limitations

prevent their inclusion in the overview tables for each dimension of child well-being.The OECD average is an unweighted average for the 24 countries included in the

main league table. Source: HBSC 2005-2006 (see page 30).

Healthy eating

(range 0-14)Median

Average

below the

median

Average

absolute

gap




Belgium 10.0 6.5 3.5

Canada 10.0 6.3 3.7

Poland 8.5 5.3 3.2

France 8.5 5.3 3.2

Sweden 8.5 5.2 3.3


Norway 8.5 5.1 3.4

Portugal 8.5 5.0 3.5



Greece 8.5 4.9 3.6

Slovakia 8.5 4.8 3.7Luxembourg 8.5 4.7 3.8

Germany 8.5 4.7 3.8

Denmark 10.0 5.6 4.4

Austria 7.3 4.0 3.3

Spain 8.0 4.3 3.7

Italy 8.5 4.6 3.9


Ireland 10.0 5.3 4.7

Iceland 8.5 4.5 4.0

Finland 8.0 4.1 3.9

Hungary 7.3 3.6 3.7



Turkey 8.5 5.3 3.2



17/40

Turkey


OECD average

France

Spain

Italy

Poland

Hungary

Sweden

Belgium

United States

Austria

GreeceCanada

Czech Republic

United Kingdom

Luxembourg

Denmark

Slovakia

Iceland

Finland

Germany

Ireland

Norway

Switzerland

Netherlands

54.6

32.3

43.6

42.0

41.9

41.0

35.8

35.5

35.3

34.9

34.1

33.833.2

33.0

32.1

31.1

29.9

28.4

26.8

26.7

25.2

24.9

24.7

24.4

24.1

0 10 20 30 40 50 60

activity (on a scale of 0 to 11).

Following the pattern already

established, Figs. 4b and 4c measure

inequality by the gap between the

median score of each country and the

average score for all children below

the median. The bar charts again

show each countrys inequality gap asa percentage of the national median.

For healthy eating, the lowest level

of inequality is to be found in the

Netherlands, Belgium and Canada

and the highest in Iceland, Finland

and Hungary.

For vigorous physical activity, the

Netherlands again has least inequality,

closely followed by Switzerland and

Norway. The highest levels of bottom-end inequality are to be found in

France, Italy and Spain.

Health: an overview

Figure 4d combines the three measures

of bottom-end inequality in childrens

health onto a standardized common

scale. As the bar chart shows, the

Netherlands heads the table by a

distance (with the lowest inequality in

all three indicators). The United States,Italy and Hungary show the highest

levels of bottom-end inequality in

childrens health.

Figure 4e breaks down this overall

performance by showing the

contributions of the three individual

indicators. It allows countries like

France and Poland, for example, to see

that their position in the bottom half

of the table is brought about by high

levels of inequality in vigorous

physical activity. Ireland and Finland,

on the other hand, would both be

closer to the top of the table if it

were not for high levels of inequality

in healthy eating.

Statistics and children

This attempt at an international

comparison of inequality in different

dimensions of childrens well-beingis a work in progress. But its clear

overall message is that children are

falling signicantly further behind

in some countries than in others.

In particular, Denmark, Finland, the

Netherlands, and Switzerland are

leading the way in limiting how far

behind the least advantaged children

are allowed to fall.

Before discussing some of the

implications of these overallndings, two other concerns

should be acknowledged.

Fig. 4c ealth inequality: vigorous physical activity

The 2005-2006 HBSC survey asked 11, 13 and 15-year-old students about their exercise habits outside school hours, converting

the answers into a score for frequency of vigorous physical activity on a scale of 0 (no vigorous physical activity) to 11 (frequent

vigorous physical activity).




Notes: Data for Portugal are missing. Other OECD countries are listed separately

because data limitations prevent their inclusion in the overview tables for eachdimension of child well-being. The OECD average is an unweighted average for

the 23 countries included in the main league table. Source: HBSC 2005-2006 (see page 30).

Vigorous physical activity

(range 0-11)Median

Average

below the

median

Average

absolute

gap





Norway 7.0 5.3 1.7

Ireland 7.0 5.3 1.7

Germany 7.0 5.2 1.8

Finland 8.0 5.9 2.1

Iceland 7.0 5.1 1.9

Slovakia 8.0 5.7 2.3

Denmark 8.0 5.6 2.4

Luxembourg 7.0 4.8 2.2



Canada 8.0 5.3 2.7Greece 7.0 4.6 2.4

Austria 7.0 4.6 2.4


Belgium 7.0 4.5 2.5

Sweden 7.0 4.5 2.5

Hungary 7.0 4.5 2.5

Poland 6.0 3.5 2.5

Italy 7.0 4.1 2.9

Spain 6.0 3.5 2.5

France 7.0 3.9 3.1



Turkey 6.0 2.7 3.3



18/40

Hungary

Italy

United States

Spain

Greece

Luxembourg

Poland

Iceland

Finland

France

Sweden

Austria

Slovakia

United Kingdom

Czech Republic

Canada

Denmark

Ireland

Belgium

Switzerland

Germany

Portugal

Norway

Netherlands

85 90 95 100 105 110 115 120


OECD-24 average

inequality close to

OECD-24 average


OECD-24 average

Hungary

Italy

United States

Spain

Greece

Luxembourg

Poland

Iceland

Finland

France

Sweden

Austria

Slovakia

United Kingdom

Czech Republic

Canada

Denmark

Ireland

Belgium

Switzerland

Germany

Portugal**

Norway

Netherlands

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0


OECD-24 average


OECD-24 average

vigorous physical activity

healthy eating

health complaints

First, in measuring different

dimensions of childrens well-being it

is necessary to separate outcomes that

are rarely separated in childrens lives.

Multiple disadvantage is the norm

with each dimension intimately linked

and often mutually reinforcing at the

level of the individual childs life.

Second, the perennial danger of all

statistics is that in offering an overview

they can seem very distant from the

realities they seek to capture. And in

presenting these data, UNICEFs plea

is that the children themselves should

as far as possible be seen not as

statistics but as individual young

people, each with a name and a face,

each with needs and rights, each with

a personality and a potential, each

with a capacity to benet from and

contribute to the societies into which

they are born, and each with a keen

awareness of the norms of the societies

in which they live.

Fig. 4d ealth inequality: an overview


health well-being (self-reported health complaints, healthy eating,

and vigorous physical activity) into an overview for the 24 OECD

countries with available data. For each country, the inequality

scores for the three indicators of health well-being have been

standardized, averaged and placed on a common scale in which

100 represents the OECD average and 10 is equal to one

standard deviation.*

Fig. 4e ealth inequality: a breakdown

Figure 4e presents the same information as Figure 4d but shows

the individual contributions of the three inequality indicators used.

For each indicator, the length of the bar represents each country's




* A standard deviation is a measure of the spread of the distribution aroundits average.

Sources: See individual Figs. 4a, 4b, and 4c. See also Fig 4e for the standardized

inequality measure used for the three individual indicators of inequality in child

well-being.

** Data on vigorous physical activity for Portugal are missing.Sources: See individual Figs. 4a, 4b, and 4c.



19/40

A case has been made that national

averages are an insufcient guide to

national performance in meeting

childrens needs. Equity measures,and in particular measures of bottom

end inequality, are also needed. (The

UNICEF global report on Progress

for Children 2010makes a similar case

for the inclusion of equity measures

in monitoring the Millennium

Development Goals).

Secondly, it has been argued that

falling behind has many mutually

reinforcing dimensions and can not

be adequately represented by anysingle indicator. Policies to prevent

children from falling behind must

therefore address the different

dimensions of disadvantage

individually as well as collectively.

Such policies are largely a matter for

national research and debate. But an

international perspective can perhaps

offer some insights into this under-

researched area.

Equity in education

For the purposes of reducing bottom-

end inequality in childrens educational

achievement, for example, it is clear

that school admissions policies can

make a difference.

In all OECD countries where studies

have been conducted,x the average

socio-economic level of students in

a particular school has been found

to have an effect on educationalachievement that is over and above

the effects associated with the

socio-economic status of the individual

student. This nding strongly suggests

that pupils from lower socio-economic

backgrounds benet from attendingschools in which a wide range of

home backgrounds are represented.

Conversely, falling behind is

signicantly more likely when students

from homes of low socio-economic

status attend schools in which the

average socio-economic status is

also low.xi

The reasons for this school

composition effect are many. Schools

with low socio-economic proles maynd themselves struggling against lower

expectations on behalf of both staff and

students; the ethos and disciplinary

climate may be less conducive to

learning; pupil-teacher relations may

be less positive; parental involvement

and support may be weaker; and the

task of attracting and retaining the

most able teachers may be more

difcult. All of these are formidable

barriers to learning.

In many OECD countries there are

signicant numbers of schools in which

the average socio-economic prole is

below the 20th percentile of the socio-

economic distribution for the OECD

as a whole.xii In such cases, the school

composition effect is enlisted against

rather than in favour of those who are

already most at risk of educational

underachievement. The likely result is

an increase in bottom-end inequality.

Two obvious approaches may counter

this effect. First, the attempt can be

made to boost the performance of

low socio-economic status schools

(for example by increasing the

resources available to them andallowing them to offer extra incentives

to more able teachers). Second,

admission policies can be designed to

avoid the concentration of pupils from

disadvantaged backgrounds in low

socio-economic status schools. This

might be achieved, for example, by

admitting children in ability bands

without regard to socio-economic

background. Policies designed to

monitor and balance the socio-

economic prole of pupil intakemay also be important. As a 2006

report commissioned by UNESCO

has pointed out:

Countries with high levels of segregation

along socio-economic lines tend to have

lower overall performance and greater

disparities in performance between

students from high and low socio-economic

backgrounds

In countries with high levels of socio-economic segregation, policies that aimed

to reduce socio-economic segregation through

compensatory reforms would likely bring

considerable gains in raising and leveling

the learning bar.xiii

In practice, a combination of both

approaches will be necessary in

countries with high bottom end

inequality in educational outcomes

(shown in Figure 3d where the

bottom ve countries are Belgium,France, Austria, Italy and Germany).

Part 2



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Resistance to such policies is common

and is often based, at least in part, on

fears that overall educational outcomes

might be adversely affected. But the

international comparisons set out in

Figs. 3f(i) and 3f(ii) suggest that lower

bottom-end inequality need not imply

any lowering of standards for high-achieving students. As the report for

UNESCO already cited concluded:

Successful schools tend to be those that

bolster the performance of those from less

advantaged backgrounds. Similarly, countries

that have the highest levels of performance

tend to be those that are successful in not

only raising the learning bar but also

leveling it.xiv

Equity in healthHigher than average inequality in

childrens health may also need to

be addressed by specic health sector

policies. Depending on context, such

policies might include: the targeting

of resources and outreach programmes

on those most at risk; the setting of

specic disparity reduction targets

for key health indicators such as

obesity, exercise, healthy eating and

infant and child mortality rates; and

increasing the reach and renement ofprevention policies designed to reduce

the impact of the health behaviours

that contribute most to bottom-end

inequality in health outcomes (such as

obesity, drug and alcohol abuse, and

smoking).

But there are also clear dangers in a

sectoral approach to reducing bottom-

end inequalities in childrens health.

It is tempting to target the bottom endof the distribution with policies aimed

at specic changes in lifestyles such as

promoting exercise and healthy eating

or reducing smoking or obesity levels.

But necessary as such programmes

are,* they cannot address the fact that

inequality in health outcomes, as in

educational outcomes, is principally

driven by socio-economic status.xv

A 2010 review of health inequalities in

the United Kingdom and what can

be done about them has this to say:

Inequalities in heath arise because of

inequalities in society in the conditions in

which people are born, grow, live, work, and

age. So close is the link between particular

social and economic features of society andthe distribution of health among the

population, that the magnitude of health

inequalities is a good marker of progress

towards creating a fairer society. Taking

action to reduce inequalities in health does

not require a separate health agenda, but

action across the whole of society.xvi

The signicance of the social gradient

in health has been demonstrated by

a steady ow of research ndings in

many OECD countries over recentyears.xvii Taking the three indicators of

inequality in childrens health used in

Part 1 of this report, for example, the

detailed HBSC data clearly show that

children of more afuent families take

more regular exercise, have healthier

eating habits, and report fewer health

problems.xviii Socio-economic status,

it is worth reminding ourselves, is

neither the choice nor the

responsibility of the child.

Among other studies, particularly

striking is the nding in Canada that

exposure to poverty in childhood

doubles the risk of death by age 55.xix

Similarly in the United States, socio-

economic status in childhood has been

shown to be a powerful predictor of

cardio-vascular disease in later life.xx

In Europe, the 2006 report Health

Inequalities: Europe in Proleconcludes

that, across the board, the poor have

shorter lives and more years of illhealth. Socio-economic inequalities in

health, says the reports author, Johan

Mackenbach of Rotterdam Universitys

Medical Centre, are unacceptable, and

represent one of Europes greatest challenges

for public health.xxi

Yet it is clear from the data presented

here and elsewhere that the relationship

between socio-economic status and

health is not xed. Being of low socio-

economic status clearly carries a greater

degree of risk in some countries than

in others. In most OECD countries,

for example, children born to parents

with low levels of education or into

homes with low socio-economic status

are more likely to die in the rst twelvemonths of life. Yet the steepness of this

social gradient in infant mortality

rates varies considerably from country

to county.xxii

Some countries, therefore, are clearly

doing a better job than others either

in reducing socio-economic inequalities

or in mitigating their impact on

childrens health and development. And

again it is the case that the countries

with the highest median levels of health the Netherlands, Austria, and Portugal

also have the lowest levels of health

inequality (Figure 4a). Conversely, the

countries whose children have the

lowest average levels of self-reported

health all tend to have higher-than-

average levels of health inequality.

The importance of income

Socio-economic status is therefore the

indispensable framework for policy

analysis of bottom-end inequality forchildren. For just as inequalities in

heath reect not only the effect of

health services but also the conditions

in which people are born, grow, live, work,

and age, so inequalities in educational

outcomes at age 15, for example, reveal

not only what happens in schools

but also the educational resources,

stimulation and encouragement that

surrounds a child from the earliest

weeks and months of life.

Policies designed to address specic

inequalities in health or education are

therefore likely to have limited impact

if they conne themselves to the health

and education sectors alone. The most

potent fact about children who fall

signicantly behind their peers is that,

by and large, they are the children of

families at the bottom end of the

socio-economic scale.

* In England, for example, smoking accounts for approximately half the difference in average life expectancy between the lowest and highest income groups.

(Michael Marmot (chair) 2010,Fair Societies, Healthy Lives, Strategic Review of Health Inequalities in England post 2010, p 10.)



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Most OECD countries have adopted national poverty

lines based on a percentage of the nations median

income. The European Union, for example, draws the

poverty line at 60% of the median. The OECD uses

50% of the median.1

In some countries, the idea of relative poverty is still

a matter of dispute. Poverty, it is argued, should be

measured by absolute rather than relative standards.

In the United States, for example, the ofcial poverty

line is based on a multiple of the income required to

ensure an adequate diet.

But it could be argued that all denitions of poverty

other than the minimum required for sheer physicalsurvival are in fact relative denitions. Absolute

poverty in the not-too-distant past, for example, meant

that life was nasty, brutish and short. Absolute poverty

in the United States today means not being able to

afford a standard of living including standards of

nutrition, water supply, sanitation, health care and

transport far in advance of the standards enjoyed by

most of the worlds population for most of its history.

In this sense, even absolute denitions of poverty are

really relative denitions that eventually have to be

updated to take account of changing standards of what

is acceptable to the society as a whole. The questionthen becomes whether the denition should be updated

infrequently in anad hoc way or whether it should be

updated regularly and systematically for example by

tying it to the national median income.

In recent times, dening income poverty in relative

terms has become widely established, especially in the

European Union. In the United Kingdom, for example,

TheEconomistmagazine notes that A decade ago, the

prospect of the Conservatives accepting the idea of

relative poverty rather than an absolute measure of

want, such as a basket of goods that every household

should be able to afford would have been fanciful.

Nowadays, it is a reality.2

Box 1 Child poverty: a relative measure

1 In discussing child poverty rates, part 2 of this report follows

the method recommended by the OECD, drawing the poverty line

at 50% of national median household income. Household income

is taken to mean disposable household income, i.e. after taxes

and public transfers. This is then equivalized to take into account

the economies of scale available to different sizes of households

(using the square root of household size). The poverty line is

therefore dened as half of the median national disposable

equivalized income; the child poverty rate is then calculated on

the same basis but taking into account only households with

children aged 0-17.

2 Still with us, The Economist, 1 July 2010.

This is not to say that the idea is new. More than 200

years ago the founding father of modern economics

argued that poverty was a relative concept:

By necessaries I understand, not only the commodities

which are indispensably necessary for the support of

life, but whatever the custom of the country renders

it indecent for creditable people, even of the lowest

order, to be without. A linen shirt, for example, is,

strictly speaking, not a necessary of life. But in the

present times, through the greater part of Europe, a

creditable day-labourer would be ashamed to appear

in public without a linen shirt ... Custom, in the same

manner, has rendered leather shoes a necessary of life

in England. The poorest creditable person of either sex

would be ashamed to appear in public without them.

Under necessaries, therefore, I comprehend, not

only those things which nature, but those things which

the established rules of decency have rendered

necessary to the lowest rank of people.

Adam Smith, An Enquiry into the Nature and Causes

of the Wealth of Nations, Book 5, Chapter 2, 1776.



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Action to prevent children from falling

behind in different dimensions of well-

being must therefore eventually come

face to face with the question of the

socio-economic gradient.

Income poverty

Socio-economic status is about morethan income. A familys past savings

and future prospects, housing and

neighbourhoods, levels of parental

education and expectation, and status

in relation to the mainstream or

established ethnic or linguistic

community all of these enter into

the socio-economic equation. Yet

of the available measures, the most

important single guide to, and

predictor of, a familys socio-economic

status remains its level of householdincome. Reducing bottom-end

inequality in incomes will not solve

all other problems, but it will make

their solution easier. Climbing the

socio-economic ladder is more feasible

if the rungs are closer together.

Reviewing many studies that show

a strong and consistent associationbetween relative income poverty and

falling behind, Susan Mayer makes

the point uninchingly:

Parental income is positively correlated

with virtually every dimension of child

well-being that social scientists measure,

and this is true in every country for which

we have data. The children of rich parents

are healthier, better behaved, happier and

better educated during their childhood and

wealthier when they have grown up thanare children from poor families.xxiii

Relative income poverty therefore

occupies a primus inter pares position

among the indicators of falling

behind. But monitoring of income

poverty that can exert such leverage

over the trajectories of childrens lives

is not simply a matter of calculating

what proportion of a nations childrenis growing up in households whose

income falls below a given threshold.

The depth, duration and timing of that

poverty in relation to the different

stages of a childs development may

also be critical. A 2007 Canadian

review of research into this issue

renes the point:

Studies that measure family income over

extended periods of time and include

changes in income and the depth of incomeinequality in their models and analysis

The time lag between the gathering of data through

sample surveys in different countries and the publishing

of that data in internationally comparable form is

approximately 3 years. Most of the data in this report

therefore apply to the years 2006 to 2008.

Normally, such a delay is no more than frustrating.

Socio-economic data of a kind used here tend to reect

long-term trends rather than year on year changes.

But much has changed in the world since 2008. Economic

recession has affected millions in the OECD countries.

The response of governments, whether by cuts in

spending or increases in taxation, is affecting many

millions more. Across the European Union, for example,unemployment is predicted to surpass the 10% mark by

the time this report is published. This means that

approximately 5 million more people will be unemployed

than before the crisis began. As joblessness is a principal

driver of poverty, it is likely that the material well-being of

children has deteriorated in some countries since 2008.

In the European Union, youth unemployment, in particular,

has risen from a pre-crisis level of under 15% to more

than 20% today.1 Many households have seen their

incomes fall and are facing difculties with debt

repayments. In the United States, as many as half of all

workers have taken a cut in pay or hours or suffered atleast temporary unemployment in the two half years since

Box 2 First call: children and recession

the crisis struck.2 Migrant workers and those on short-term

contracts are particularly vulnerable.

There may be worse to come. According to a report by

the European Union Social Protection Committee The full

impact of the crisis on labour markets and public nances

has yet to be faced.3

In other words, the snapshot of inequality in childrens

well-being presented in these pages is a snapshot taken

in good times.

No overall statistics are yet available to chart the impact

of recession on the children of the poorest families. But a

partial glimpse may be offered by the changing demands

on charities and government special assistance

programmes. The International Federation of Red Cross and

Red Crescent Societies, for example, is reporting increasing

numbers of people seeking help with the basic necessities

of life including some who would never normally think of

seeking help from a charitable body.4

In the United States,

the number of people receiving SNAP benets (under the

Supplementary Nutritional Assistance Program) has risen

by almost a quarter since the crisis began (from 29.5 million

to 36.5 million people a month in the year to August 2009).

Approximately half of all SNAP beneciaries are children.5

It is also worrying that the Eurochild report is beginning to

show increases in demands on child protection servicesin a number of European countries.6



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In its work with children in the developing world, UNICEF

has long experience of what happens to the vulnerable

when economies turn down. Through the second half of

the 1980s and the early 1990s, for example, many of the

worlds poorest nations entered a period of economic

adjustment which included cuts in government spending

on basic services and subsidies on which the poor were

most dependent.

Throughout that period, UNICEF urged special action to

prevent the heaviest burden from falling on those least

able to bear it.

That same argument now needs to be made to some of

the worlds richest economies.

In hard times, the poorest children should be the rst

to be protected, not the last to be considered. A child

has only one chance to develop normally in mind and

body. And it is a primary responsibility of governments

to protect that chance in good times and in bad.

In practice, this means that protecting children during the

critical e

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